BÜCHER VERSANDKOSTENFREI INNERHALB DEUTSCHLANDS
Computational Methods for Deep Learning
Theory, Algorithms, and Implementations
2nd ed. 2023
Springer
ISBN 9789819948222
Standardpreis
Bibliografische Daten
Fachbuch
Buch. Hardcover
2nd ed. 2023. 2023
4 s/w-Abbildungen, 36 Farbabbildungen, Bibliographien.
In englischer Sprache
Umfang: xx, 222 S.
Format (B x L): 15,5 x 23,5 cm
Gewicht: 535
Verlag: Springer
ISBN: 9789819948222
Weiterführende bibliografische Daten
Das Werk ist Teil der Reihe: Texts in Computer Science
Produktbeschreibung
The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book.
The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI).
This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI).
This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.
Autorinnen und Autoren
Kundeninformationen
Explores advanced topics in deep learning encompassing transformer models, control theory, and graph neural networks Presents detailed mathematical descriptions and algorithms for generative pre-trained models, such as GPTs Serves as a valuable reference book for postgraduate and PhD students
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